For meta-analysis of intervention studies,
[1]}
designed a likelihood based sensitivity analysis,
in which the selection probability of each study was modeled
as a function of \(t\) -type statistic or \(p\) -value
for the primary outcome.
Extending [1]} into meta-analysis of diagnostic test accuracy,
we embed a cutoff-dependent selection function to
the bivariate normal model of
[3]}
(hereinafter referred to as Reistma model)
and propose a sensitivity analysis model
to evaluate the impact of publication bias on SROC and SAUC.
Our selection function allows the selection probability
of each study to be determined by \(t\) -type statistic
for a linear combination of
logit-transformed sensitivity and specificity.
It can handle situations where
the selective publication process is influenced by
\(t\) -type statistic for sensitivity, specificity,
or diagnostic odds ratio (DOR),
and would be an appealing alternative to
the existing Heckman-type selection functions.
